Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

Currin, Andrew; Swainston, Neil; Day, Philip J.; Kell, Douglas B.

doi:10.1039/c4cs00351a

Cited by 337 publications

(266 citation statements)

References 1,317 publications

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“…The assumption of randomness in biology generally holds well enough to serve as a useful approximation, but in fact biological variation is not genuinely random; for example, a bias might arise due to mutagenic agents, assortative mating, or in the case of synthetic biology, directed mutation (e.g., Currin, Swainston, Day, & Kell, 2015). Natural selection works by generating numerous possibilities through a process that can be approximated by a random distribution, such that often enough at least one variant is bound by chance to be fitter than what came before, and likely to be amplified in subsequent generations.…”

Section: Biased Variation/directed Mutationmentioning

confidence: 99%

Toward an evolutionary-predictive foundation for creativity

Gabora

Kauffman

2015

Psychon Bull Rev

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Dietrich and Haider (Psychonomic Bulletin & Review, 21 (5), 897-915, 2014) justify their integrative framework for creativity founded on evolutionary theory and prediction research on the grounds that Btheories and approaches guiding empirical research on creativity have not been supported by the neuroimaging evidence.^Although this justification is controversial, the general direction holds promise. This commentary clarifies points of disagreement and unresolved issues, and addresses mis-applications of evolutionary theory that lead the authors to adopt a Darwinian (versus Lamarckian) approach. To say that creativity is Darwinian is not to say that it consists of variation plus selection -in the everyday sense of the term -as the authors imply; it is to say that evolution is occurring because selection is affecting the distribution of randomly generated heritable variation across generations. In creative thought the distribution of variants is not key, i.e., one is not inclined toward idea A because 60 % of one's candidate ideas are variants of A while only 40 % are variants of B; one is inclined toward whichever seems best. The authors concede that creative variation is partly directed; however, the greater the extent to which variants are generated non-randomly, the greater the extent to which the distribution of variants can reflect not selection but the initial generation bias. Since each thought in a creative process can alter the selective criteria against which the next is evaluated, there is no demarcation into generations as assumed in a Darwinian model. We address the authors' claim that reduced variability and individuality are more characteristic of Lamarckism than Darwinian evolution, and note that a Lamarckian approach to creativity has addressed the challenge of modeling the emergent features associated with insight.Keywords High order cognition . Creativity . Darwinian evolution . Communal exchange . Selection . Dual process . Neuroscience . Prediction . Cognitive neuroscience As Dietrich and Haider (2014) suggest, synthesizing creativity into an integrated framework that incorporates evolutionary theory and literature on prediction could enrich our understanding of how this mental faculty has transformed human civilizations. This commentary briefly addresses their negative pronouncement on efforts toward a neuroscience of creativity, clears out some errors in their application of evolutionary theory to creativity, and clarifies points of disagreement and unresolved issues in order to pave the way for an integrated understanding of how the creative process works. Prospects for a neuroscience of creativityThe rationale Dietrich and Haider give for embarking on the research program described in this paper is that Ball theories and approaches guiding empirical research on creativitydivergent thinking, defocused attention, right brains, low arousal, prefrontal activation, alpha enhancement, etc. -have not been supported by the neuroimaging evidence.^We believe it is important to point out ...

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Section: Biased Variation/directed Mutationmentioning

confidence: 99%

Toward an evolutionary-predictive foundation for creativity

Gabora

Kauffman

2015

Psychon Bull Rev

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“…So, it would appear that natural enzymes are "just good enough" for the function they must perform in a given organism [7]. One might conclude that if they had evolved to their optimum performance then trying to improve them (from a kinetic point of view) would be attempting the impossible.…”

Section: Introductionmentioning

confidence: 99%

“…These are often limited only by the rate of ligand diffusion into the active site (diffusion-controlled rate). Nevertheless, an extensive analysis by Bar-Even et al, of nearly 2000 enzymes, showed that the median maximal turnover rate value over all measured enzymes is about 10 s −1 nowhere close to the values of 10 5 or 10 6 normally associated with catalysts [5,6].So, it would appear that natural enzymes are "just good enough" for the function they must perform in a given organism [7]. One might conclude that if they had evolved to their optimum performance then trying to improve them (from a kinetic point of view) would be attempting the impossible.…”

mentioning

confidence: 99%

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Monza

Acebes

Lucas

et al. 2017

Directed Enzyme Evolution: Advances and Applications

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Directed evolution creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-319-50413-1_10/fulltext.html of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of art of molecular modeling, discussing their strengths, limitations and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques, and a synergic effort between simulations and experimental validation. IntroductionBiotechnology needs catalysts that can work under harsh conditions, catalyze a broad range of substrates, generate maximum amount of product, and tolerate changes in the environment. Enzymes, which are biodegradable and reusable catalysts [1], in addition to remarkable reaction rates, can work in environmentally friendly pH and temperature ranges, and display control over stereochemistry and regioselectivity which makes them ideal for many applications [2,3]. When thinking about enzymes, people normally associate them to expressions such as "perfect catalysts" or "outstanding reaction rate". In fact, there are examples of enzymes that catalyze reactions at extremely high rates such as triose phosphate isomerase, superoxide dismutase or carbonic anhydrase [4]. These are often limited only by the rate of ligand diffusion into the active site (diffusion-controlled rate). Nevertheless, an extensive analysis by Bar-Even et al., of nearly 2000 enzymes, showed that the median maximal turnover rate value over all measured enzymes is about 10 s −1 nowhere close to the values of 10 5 or 10 6 normally associated with catalysts [5,6].So, it would appear that natural enzymes are "just good enough" for the function they must perform in a given organism [7]. One might conclude that if they had evolved to their optimum performance then trying to improve them (from a kinetic point of view) would be attempting the impossible. On the contrary, as seen by the distribution of reaction rates, k cat , most enzymes function at a lower rate than the diffusion-limit and thus, there is space to further increase their kinetic properties to meet industrial needs. Additionally, we need enzymes capable of catalyzing reactions for which no known enzymes exist, to work with different substrates and for particular conditions that are industrially...

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“…The sequence complexity is fundamental in protein systems [32,34]. For a typical size of protein domains, say 100 amino acids, there are about 20 100 (∼ 10 130 ) possible sequences with the alphabet built with 20 kinds of amino acids.…”

Section: Sequence-structure Relationship and Diversity Of Sequencesmentioning

confidence: 99%

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

Wang

2016

Advances in Physics: X

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Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

Abstract: Improving enzymes by directed evolution requires the navigation of very large search spaces; we survey how to do this intelligently.

Cited by 337 publications

References 1,317 publications

Toward an evolutionary-predictive foundation for creativity

Toward an evolutionary-predictive foundation for creativity

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

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